结核菌菌感染的数字双胞胎:整合免疫动态和病原体适应,用于精确治疗
Ruqaiyyah Siddiqui1, Naveed Ahmed Khan2
1Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot-Watt University, Edinburgh EH14 4AS, UK; Microbiota Research Center, Istinye University, Istanbul 34010, Turkey.
Journal of infection and public health
|February 6, 2026
概括
结核病的数字双胞胎框架整合了各种数据,以模拟疾病的进展和预测治疗结果. 这种计算模型旨在个性化治疗,并推进对Mycobacterium结核病感染的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 传染病建模传染病建模
- 精准医学是一门精准的医学.
背景情况:
- 结核菌每年造成超过一百万人的死亡,疾病进展因宿主-病原体相互作用而变化.
- 当前的研究在预测个人对结核病治疗的反应方面面临挑战.
研究的目的:
- 为结核病 (TB) 提出一个数字双胞胎框架.
- 将临床,免疫学和药物动力学数据集成到一个自适应的计算模型中.
- 模拟结核病管理的宿主-病原体动态和个性化药物反应.
主要方法:
- 开发一个持续适应的计算模型 (数字双胞胎).
- 临床,免疫学和药物动力学数据的整合.
- 模拟宿主-病原体动态,从颗粒瘤形成到免疫调节.
- 将模拟过程与个性化药物暴露和治疗反应联系起来.
主要成果:
- 该框架预测结核病的结果,并确定复发或耐药性的早期指标.
- 它模拟了宿主-病原体的动态,并将它们与药物暴露和治疗反应联系起来.
- 数字双胞胎是感染生物学,计算和临床翻译之间的桥梁.
结论:
- 拟议的结核病数字双胞胎可以指导精确治疗,并加速发现宿主导干预措施.
- 这种不断发展的模型有可能改变对结核病的理解和管理.
- 为了成功翻译,需要进一步的纵向数据和现实世界的验证.
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